Predictive tools for stabilization of therapeutic proteins

Voynov, Vladimir; Chennamsetty, Naresh; Kayser, Veysel; Helk, Bernhard; Trout, Bernhardt L.

doi:10.4161/mabs.1.6.9773

Cited by 66 publications

(73 citation statements)

References 18 publications

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“…In silico optimization can be used for many other properties of antibodies required for successful development. Properties such as viscosity, aggregation propensity, chemical instability, phase separation and reduced clearance rates benefit from a co-crystal structure for direct prediction, [73][74][75][76] as well as to prevent the introduction of destabilizing and affinity reducing mutations. Additionally, as described previously, determining the epitope of interaction can be useful in understanding the biological activity and function of the antibody.…”

Section: Discussionmentioning

confidence: 99%

Beyond CDR-grafting: Structure-guided humanization of framework and CDR regions of an anti-myostatin antibody

Apgar

Mader

Agostinelli

et al. 2016

mAbs

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Antibodies are an important class of biotherapeutics that offer specificity to their antigen, long half-life, effector function interaction and good manufacturability. The immunogenicity of non-human-derived antibodies, which can be a major limitation to development, has been partially overcome by humanization through complementarity-determining region (CDR) grafting onto human acceptor frameworks. The retention of foreign content in the CDR regions, however, is still a potential immunogenic liability. Here, we describe the humanization of an anti-myostatin antibody utilizing a 2-step process of traditional CDR-grafting onto a human acceptor framework, followed by a structure-guided approach to further reduce the murine content of CDR-grafted antibodies. To accomplish this, we solved the co-crystal structures of myostatin with the chimeric (Protein Databank (PDB) id 5F3B) and CDR-grafted antimyostatin antibody (PDB id 5F3H), allowing us to computationally predict the structurally important CDR residues as well as those making significant contacts with the antigen. Structure-based rational design enabled further germlining of the CDR-grafted antibody, reducing the murine content of the antibody without affecting antigen binding. The overall "humanness" was increased for both the light and heavy chain variable regions.

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Section: Discussionmentioning

confidence: 99%

Beyond CDR-grafting: Structure-guided humanization of framework and CDR regions of an anti-myostatin antibody

Apgar

Mader

Agostinelli

et al. 2016

mAbs

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“…Such simulations require large computing resources. Hence, only a few atomistic molecular dynamics (MD) simulations on a single full-length antibody molecule in explicit solvent have been performed, [51][52][53][54][55] and only one study describing atomistic simulations on two full-length antibody molecules in the simulation box, to study pairwise intermolecular interactions, has been reported so far. 56 While atomistic MD simulations of solutions containing several full length antibody molecules are currently impractical, coarse-grained MD simulations can provide a faster, but less accurate, understanding of intermolecular interactions in antibody solutions.…”

Section: Multi-scale Molecular Simulations Of Antibody Solutions To Umentioning

confidence: 99%

Molecular basis of high viscosity in concentrated antibody solutions: Strategies for high concentration drug product development

Tomar

Kumar

Singh

et al. 2016

mAbs

147

145

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Effective translation of breakthrough discoveries into innovative products in the clinic requires proactive mitigation or elimination of several drug development challenges. These challenges can vary depending upon the type of drug molecule. In the case of therapeutic antibody candidates, a commonly encountered challenge is high viscosity of the concentrated antibody solutions. Concentration-dependent viscosity behaviors of mAbs and other biologic entities may depend on pairwise and higher-order intermolecular interactions, non-native aggregation, and concentration-dependent fluctuations of various antibody regions. This article reviews our current understanding of molecular origins of viscosity behaviors of antibody solutions. We discuss general strategies and guidelines to select low viscosity candidates or optimize lead candidates for lower viscosity at early drug discovery stages. Moreover, strategies for formulation optimization and excipient design are also presented for candidates already in advanced product development stages. Potential future directions for research in this field are also explored.

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“…Substantial progress has been made in computational prediction of thermal/pH stability, 6 aggregation propensity, 7,8 viscosity 9,10 and in-vivo clearance of mAbs. 9,11 Other antibody liabilities due to chemical stress in the manufacturing process include the deamidation of asparagine (Asn), isomerization of aspartic acid (Asp) and oxidation of methionine (Met) and tryptophan (Trp) residues.…”

Section: Introductionmentioning

confidence: 99%

Prediction of methionine oxidation risk in monoclonal antibodies using a machine learning method

Sankar¹,

Hoi²,

Yuan

et al. 2018

mAbs

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Monoclonal antibodies (mAbs) have become a major class of protein therapeutics that target a spectrum of diseases ranging from cancers to infectious diseases. Similar to any protein molecule, mAbs are susceptible to chemical modifications during the manufacturing process, long-term storage, and in vivo circulation that can impair their potency. One such modification is the oxidation of methionine residues. Chemical modifications that occur in the complementarity-determining regions (CDRs) of mAbs can lead to the abrogation of antigen binding and reduce the drug’s potency and efficacy. Thus, it is highly desirable to identify and eliminate any chemically unstable residues in the CDRs during the therapeutic antibody discovery process. To provide increased throughput over experimental methods, we extracted features from the mAbs’ sequences, structures, and dynamics, used random forests to identify important features and develop a quantitative and highly predictive in silico methionine oxidation model.

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Predictive tools for stabilization of therapeutic proteins

Cited by 66 publications

References 18 publications

Beyond CDR-grafting: Structure-guided humanization of framework and CDR regions of an anti-myostatin antibody

Beyond CDR-grafting: Structure-guided humanization of framework and CDR regions of an anti-myostatin antibody

Molecular basis of high viscosity in concentrated antibody solutions: Strategies for high concentration drug product development

Prediction of methionine oxidation risk in monoclonal antibodies using a machine learning method

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